Tag Archives: machine learning

Altus SDK for Java

Categories: Altus

We are excited to announce the general availability of Cloudera Altus SDK for Java to programmatically leverage the Altus platform-as-a service for ETL, batch machine learning, and cloud bursting. Altus empowers customers and partners alike, to run data engineering workloads in the cloud, leveraging cloud infrastructures such as AWS. Cloudera Altus also provides the ability to create data engineering pipelines using both a web console and CLI.

Cloudera Altus SDK for Java was developed to provide easier programmatic access with the popular Java programming language so that users can automate their data engineering workloads.

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Production Recommendation Systems with Cloudera

Categories: CDH Data Science

Many types of business problems boil down to making recommendations, and machine learning is the special sauce that makes these problems solvable. Machine learning for recommendations is a challenging endeavor in its own right, but it is just one part of the recommendation system, which must move, store, process, and update data, in production, across several different components. In this post we show how to use Cloudera’s distribution of open source software to build a production scale recommendation system,

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Apache Spot (incubating) and Cloudera on AWS in 60 Minutes

Categories: CDH Cloud Cloudera Director

For the Apache Spot novice or for quick evaluation of a Cybersecurity solution on Cloudera Enterprise Data Hub (EDH) without the arduous tasks of manual installation, we’ve created a rapid deployment of Apache Spot on Amazon Web Services (AWS) using Cloudera Director.

You will immediately see how you can isolate and identify suspicious activities from the Apache Spot UI using the sample data provided in the deployment at cloud scale.

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Large-Scale Health Data Analytics with OHDSI

Categories: CDH Data Science

Data analytics is increasingly being brought to bear to treat human disease, but as more and more health data is stored in computer databases, one significant challenge is how to perform analyses across these disparate databases. In this post I take a look at the Observational Health Data Sciences and Informatics (or OHDSI, pronounced “Odyssey”) program that was formed to address this challenge, and which today accounts for 1.26 billion patient records collectively stored across 64 databases in 17 countries.

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